How do artificial intelligence applications affect the labor income share?New challenges to common prosperity in the digital economy era

Qiulin Wang and Jinmiao Hu

International Review of Economics & Finance, 2025, vol. 103, issue C

Abstract: Against the backdrop of artificial intelligence (AI) deeply empowering the real economy, technological progress has profoundly affected the income distribution structure of enterprises, with changes in the labor income share drawing wide attention. Focusing on Chinese A-share listed companies, this paper constructs a firm-level fixed effects panel regression model to empirically examine the impact of AI application on the labor income share. Furthermore, the paper introduces the level of common prosperity as a moderating variable to explore its moderating effect and heterogeneity. The results indicate that AI application significantly increases enterprises’ labor income share. Further analysis reveals that the level of common prosperity plays a positive moderating role in the relationship between AI application and labor income share. This moderating effect is more pronounced in companies located in central and western regions and in those with stronger external governance.

Keywords: Artificial intelligence application; Labor income share; Common prosperity (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1059056025006999
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:reveco:v:103:y:2025:i:c:s1059056025006999

DOI: 10.1016/j.iref.2025.104536

Access Statistics for this article

International Review of Economics & Finance is currently edited by H. Beladi and C. Chen

More articles in International Review of Economics & Finance from Elsevier
Bibliographic data for series maintained by Catherine Liu ().